| --- |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: pharma_classification |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # pharma_classification |
| |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5315 |
| - Accuracy: 0.9581 |
| - F1: 0.9506 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - training_steps: 30000 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
| | 0.0035 | 5.99 | 5000 | 0.2892 | 0.9539 | 0.9554 | |
| | 0.0137 | 11.98 | 10000 | 0.2620 | 0.9641 | 0.9600 | |
| | 0.0 | 17.96 | 15000 | 0.4022 | 0.9611 | 0.9586 | |
| | 0.0001 | 23.95 | 20000 | 0.3838 | 0.9611 | 0.9552 | |
| | 0.0 | 29.94 | 25000 | 0.4363 | 0.9575 | 0.9490 | |
| | 0.0 | 35.93 | 30000 | 0.5315 | 0.9581 | 0.9506 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.39.0.dev0 |
| - Pytorch 2.2.0+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
| |
